Association of brain age with smoking, alcohol consumption, and genetic variants

The association of the degree of aging based on the whole-brain anatomical characteristics, or brain age, with smoking, alcohol consumption, and individual genetic variants is unclear. Here, we investigated these associations through analyzing data collected for UK Biobank subjects with an age range of 45 to 79 years old. We first trained a statistical model for obtaining relative brain age (RBA), a metric describing a subject’s brain age relative to peers, based on a randomly selected training set subjects (n=2,679). We then applied this model to the evaluation set subjects (n=6,252) and further tested the association of RBA with tobacco smoking, alcohol consumption, and 529,098 genetic variants. We found that daily or almost daily consumption of smoking or alcohol was significantly associated with increased RBA (P<0.05). Interestingly, there was no significant difference of RBA among subjects who smoked occasionally, only tried once or twice, or abstained from smoking. Further, there was no significant difference of RBA among subjects who consumed alcohol 1 to 3 times a month, at special occasions only, or abstained from alcohol consumption. Among the subjects who smoked on most or all days and did not abstain from alcohol drinking, RBA increased by 0.021 years for each addition pack-year of smoking (P<0.05) and by 0.014 years for each additional gram of alcohol consumed (P<0.05). We did not identify individual genetic variation significantly associate with RBA. Further exploration of genetic variation-brain aging association is warranted, where our current genetic association statistics may serve as prior knowledge.

[1]  K O Lim,et al.  Brain gray and white matter volume loss accelerates with aging in chronic alcoholics: a quantitative MRI study. , 1992, Alcoholism, clinical and experimental research.

[2]  A. D. Roses,et al.  Association of apolipoprotein E allele €4 with late-onset familial and sporadic Alzheimer’s disease , 2006 .

[3]  A Hofman,et al.  Gender differences in the incidence of AD and vascular dementia , 1999, Neurology.

[4]  T. Cleophas Wine, beer and spirits and the risk of myocardial infarction: a systematic review. , 1999, Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie.

[5]  G Corrao,et al.  Alcohol and coronary heart disease: a meta-analysis. , 2000, Addiction.

[6]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[7]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[8]  Peter J. Gianaros,et al.  Higher blood pressure predicts lower regional grey matter volume: Consequences on short-term information processing , 2006, NeuroImage.

[9]  Frank Seifert,et al.  Smoking and structural brain deficits: a volumetric MR investigation , 2006, The European journal of neuroscience.

[10]  H. Hense,et al.  Modeling smoking history: a comparison of different approaches in the MARS study on age-related maculopathy. , 2007, Annals of epidemiology.

[11]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[12]  Griselda J. Garrido,et al.  Coronary heart disease is associated with regional grey matter volume loss: implications for cognitive function and behaviour , 2008, Internal medicine journal.

[13]  Steven C. R. Williams,et al.  Effects of acute nicotine on brain function in healthy smokers and non-smokers: Estimation of inter-individual response heterogeneity , 2009, NeuroImage.

[14]  Trevor Hastie,et al.  Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.

[15]  Stefan Klöppel,et al.  Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters , 2010, NeuroImage.

[16]  James A Hanley,et al.  Random measurement error and regression dilution bias , 2010, BMJ : British Medical Journal.

[17]  Ming D. Li,et al.  Genome-wide meta-analyses identify multiple loci associated with smoking behavior , 2010, Nature Genetics.

[18]  W. Ghali,et al.  Association of alcohol consumption with selected cardiovascular disease outcomes: a systematic review and meta-analysis , 2011, BMJ : British Medical Journal.

[19]  Philip S. Insel,et al.  Greater regional brain atrophy rate in healthy elderly subjects with a history of cigarette smoking , 2012, Alzheimer's & Dementia.

[20]  Bruce Fischl,et al.  FreeSurfer , 2012, NeuroImage.

[21]  P. Newhouse,et al.  Nicotine Treatment of Mild Cognitive Impairment: a 6-Month Double-Blind Pilot Clinical Trial , 2012, Neurology.

[22]  E D Levin,et al.  Nicotine treatment of mild cognitive impairment , 2012, Neurology.

[23]  Marisa O. Hollinshead,et al.  Identification of common variants associated with human hippocampal and intracranial volumes , 2012, Nature Genetics.

[24]  Christian Gaser,et al.  Advanced BrainAGE in older adults with type 2 diabetes mellitus , 2013, Front. Aging Neurosci..

[25]  Nick C Fox,et al.  Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease , 2013, Nature Genetics.

[26]  B. Mazoyer,et al.  Sex-related and tissue-specific effects of tobacco smoking on brain atrophy: assessment in a large longitudinal cohort of healthy elderly , 2014, Front. Aging Neurosci..

[27]  Neda Jahanshad,et al.  Whole-genome analyses of whole-brain data: working within an expanded search space , 2014, Nature Neuroscience.

[28]  C. Sudlow,et al.  UK Biobank Data: Come and Get It , 2014, Science Translational Medicine.

[29]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[30]  U. Lindenberger Human cognitive aging: Corriger la fortune? , 2014, Science.

[31]  A. Brickman,et al.  Alcohol intake and brain structure in a multiethnic elderly cohort. , 2014, Clinical nutrition.

[32]  M. Ramanathan,et al.  Cardiovascular risk factors are associated with increased lesion burden and brain atrophy in multiple sclerosis , 2015, Journal of Neurology, Neurosurgery & Psychiatry.

[33]  P. Elliott,et al.  UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age , 2015, PLoS medicine.

[34]  Thomas E. Nichols,et al.  Common genetic variants influence human subcortical brain structures , 2015, Nature.

[35]  Genotyping and quality control of UK Biobank , a large-scale , extensively phenotyped prospective resource , 2015 .

[36]  Matthew L Senjem,et al.  Age, Sex, and APOE ε4 Effects on Memory, Brain Structure, and β-Amyloid Across the Adult Life Span. , 2015, JAMA neurology.

[37]  H. Kranzler,et al.  Alcohol Dependence Genetics: Lessons Learned From Genome-Wide Association Studies (GWAS) and Post-GWAS Analyses. , 2015, Alcoholism, clinical and experimental research.

[38]  Y. Stern,et al.  Differences between chronological and brain age are related to education and self-reported physical activity , 2016, Neurobiology of Aging.

[39]  P. Matthews,et al.  Multimodal population brain imaging in the UK Biobank prospective epidemiological study , 2016, Nature Neuroscience.

[40]  Eileen Luders,et al.  Estimating brain age using high-resolution pattern recognition: Younger brains in long-term meditation practitioners , 2016, NeuroImage.

[41]  Daniel Marbach,et al.  Fast and Rigorous Computation of Gene and Pathway Scores from SNP-Based Summary Statistics , 2016, PLoS Comput. Biol..

[42]  Mark E Bastin,et al.  Ageing and brain white matter structure in 3,513 UK Biobank participants , 2016, Nature Communications.

[43]  Christian Gaser,et al.  The Effect of the APOE Genotype on Individual BrainAGE in Normal Aging, Mild Cognitive Impairment, and Alzheimer’s Disease , 2016, PloS one.

[44]  Samuel Asensio,et al.  Magnetic resonance imaging structural alterations in brain of alcohol abusers and its association with impulsivity , 2016, Addiction biology.

[45]  N. Volkow,et al.  Alcohol Affects Brain Functional Connectivity and its Coupling with Behavior: Greater Effects in Male Heavy Drinkers , 2016, Molecular Psychiatry.

[46]  Giovanni Montana,et al.  Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker , 2016, NeuroImage.

[47]  Daniel S. Margulies,et al.  Predicting brain-age from multimodal imaging data captures cognitive impairment , 2016, NeuroImage.

[48]  Julio Acosta-Cabronero,et al.  Brain-predicted age in Down syndrome is associated with beta amyloid deposition and cognitive decline , 2017, Neurobiology of Aging.

[49]  Christian Gaser,et al.  BrainAGE score indicates accelerated brain aging in schizophrenia, but not bipolar disorder , 2017, Psychiatry Research: Neuroimaging.

[50]  Lloyd T. Elliott,et al.  The genetic basis of human brain structure and function: 1,262 genome-wide associations found from 3,144 GWAS of multimodal brain imaging phenotypes from 9,707 UK Biobank participants , 2017, bioRxiv.

[51]  J. Cole,et al.  Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers , 2017, Trends in Neurosciences.

[52]  Stuart J. Ritchie,et al.  Brain age predicts mortality , 2017, Molecular Psychiatry.

[53]  William J. Astle,et al.  Risk thresholds for alcohol consumption: combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies , 2018, The Lancet.

[54]  J. Gallacher,et al.  The relationship between alcohol use and long-term cognitive decline in middle and late life: a longitudinal analysis using UK Biobank. , 2018, Journal of public health.

[55]  J. Whitwell,et al.  Alzheimer's disease neuroimaging , 2018, Current opinion in neurology.